The problem of trajectory similarity in moving object databases is a relatively new topic in the spatial and spatiotemporal database literature. Existing work focuses on the spatial notion of similarity ignoring the temporal dimension of trajectories and disregarding the presence of a general-purpose spatiotemporal index. In this work, we address the issue of spatiotemporal trajectory similarity search by defining a similarity metric, proposing an efficient approximation method to reduce its calculation cost, and developing novel metrics and heuristics to support k-most-similar-trajectory search in spatiotemporal databases exploiting on existing R-treelike structures that are already found there to support more traditional queries. Our experimental study, based on real and synthetic datasets, verifies that the proposed similarity metric efficiently retrieves spatiotemporally similar trajectories in cases where related work fails, while at the same time the proposed algorithm is shown to be efficient and highly scalable.

Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed (depth-first and best-first) algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (historical continuous or not), thus resulting in four types of NN queries. We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on two members of the R-tree family for trajectory data (namely, the TB-tree and the 3D-R-tree), we demonstrate their scalability and efficiency through an extensive experimental study using large synthetic and real datasets.

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